Predicting MHC-II binding affinity using multiple instance regression

Date
2011-01-01
Authors
EL-Manzalawy, Yasser
Dobbs, Drena
Honavar, Vasant
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Abstract

Reliably predicting the ability of antigen peptides to bind to major histocompatibility complex class II (MHC-II) molecules is an essential step in developing new vaccines. Uncovering the amino acid sequence correlates of the binding affinity of MHC-II binding peptides is important for understanding pathogenesis and immune response. The task of predicting MHC-II binding peptides is complicated by the significant variability in their length. Most existing computational methods for predicting MHC-II binding peptides focus on identifying a nine amino acids core region in each binding peptide. We formulate the problems of qualitatively and quantitatively predicting flexible length MHC-II peptides as multiple instance learning and multiple instance regression problems, respectively. Based on this formulation, we introduce MHCMIR, a novel method for predicting MHC-II binding affinity using multiple instance regression. We present results of experiments using several benchmark datasets that show that MHCMIR is competitive with the state-of-the-art methods for predicting MHC-II binding peptides. An online web server that implements the MHCMIR method for MHC-II binding affinity prediction is freely accessible at http://ailab.cs.iastate.edu/mhcmir.

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This article is from IEEE/ACM Transactions on Computational Biology and Bioinformatics 8 (2011): 1067, doi: 10.1109/TCBB.2010.94. Posted with permission.

Keywords
MHC-II peptide prediction, multiple instance learning, multiple instance regression
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